TightClust applies K-means clustering as an intermediate clustering engine. Early truncation of a hierarchical clustering tree is used to overcome the local minimum problem in K-means clustering. The tightest and most stable clusters are identified in a sequential manner through an analysis of the tendency of genes to be grouped together under repeated resampling.
- R Package
:: MORE INFORMATION
George C. Tseng and Wing H. Wong. (2005)
Tight Clustering: A Resampling-based Approach for Identifying Stable and Tight Patterns in Data.